Brute-Force Progress on AlgZoo's 432-param Interpretability Challenge

Nick Cammarata's recent article made me want to take a serious stab at interpretability myself. After a bit of consideration, I decided to work on the AlgZoo challenge that was posted here earlier. I trained an estimator on models with the same recipe as the given model. At 20-300 forward passes per network it estimates accuracy with mean squared error 9-36x lower than random sampling, but it's fitted to a population rather than derived, so I don't think it's a breakthrough. The challenge ARC's blogpost has a few small RNNs, including a mystery model called ​ that can be loaded in AlgZoo using example_2nd_argmax() . It has 432 parameters and given a sequence of 10 N(0,1) values outputs the location of the second-largest input with an accuracy of 95.3%. You can estimate the accuracy of such a model just by running lots of input through it and checking. Estimating the accuracy like that has mean squared error of if the network has an accuracy of and you try random inputs. ARC's challenge is to estimate the accuracy mechanistically in a way that matches sampling for mean squared error and compute, but realistically what we want is to be better and advance the Pareto frontier. I count